Adaptive Sensor Placement for Continuous Spaces
Authors: James Grant, Alexis Boukouvalas, Ryan-Rhys Griffiths, David Leslie, Sattar Vakili, Enrique Munoz De Cote
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In simulations we demonstrate our approach to have substantially lower and less variable regret than competitor algorithms. |
| Researcher Affiliation | Collaboration | 1PROWLER.io Ltd, Cambridge, United Kingdom 2STOR-i Centre for Doctoral Training, Lancaster University, Lancaster, United Kingdom 3Department of Physics, University of Cambridge, Cambridge, United Kingdom 4Department of Mathematics and Statistics, Lancaster University, Lancaster, United Kingdom. |
| Pseudocode | Yes | Algorithm 1 Thompson Sampling Inputs: Gamma prior parameters α, β > 0, upper truncation point λmax Iterative Phase: For t 1 For each k {1, . . . , Kt}, evaluate Hk,t(t 1) and Nk,t(t 1) and sample an index ψk,t TG(α+Hk,t(t 1), β+ t Nk,t(t 1), 0, λmax) Choose an action At At that maximises r(A) conditional on the true rate being given by the sampled ψk,t values, and observe the events in At |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | No | The paper describes generating data through simulations based on rate functions but does not use a publicly available or open dataset for training, nor does it provide concrete access information for such a dataset. |
| Dataset Splits | No | The paper performs simulations but does not describe using standard training, validation, and test dataset splits for a pre-existing dataset. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types, memory, cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., libraries, frameworks, or solvers). |
| Experiment Setup | Yes | Here, and throughout our experiments, we set the prior parameters for Thompson sampling to be α = 0.5 and β = 0.5/C, where scaling by cost C makes the prior relevant to the expected scale of costs in the problem. We also set the truncation λmax to be ten times the true maximal value of λ; λmax is an inconvenient parameter that is only needed for the theory, so we set it to a conservative large value that should have no influence on the real behaviour of the algorithm. The experiment is run 10 times for T = 1024 timesteps starting with K0 = 4 bins. |